There currently exist many technologies for determining and detecting objects on an electronic map or image, such as types of computer vision solutions, pixels analyses, manual human recognition, and the like. In addition, there are technologies for executing and training neural networks and other machine learning algorithms to detect objects on maps, for example, technologies for transforming a picture or a satellite image/map into a scheme map. Typically, machine learning algorithms are trained by a human moderator who shows examples by providing a satellite map and a respective scheme terrain map prepared by human, for example, of the same object at both maps so that the machine learning algorithm is trained as to how the same object(s) appears on different images/maps.
However, these technologies are limited in that if the image needs updating, the old image/map is required to be completely replaced with a new image or map. However, in some instances, the older image may have better quality and resolution than the new image. Moreover, the update process may require the new image to be purchased from one or more third parties that are providing the new geospatial image. At the same time, end users and consumers typically prefer up-to-date images showing the current landscape and features of the image in the case of a map or scene provided by a service like Yandex Maps or Google Maps, for example.
Accordingly, what is needed is a technology for updating original (or older) images to reflect the current conditions of the captured scene/landscape.